Islam Md Sakib Bin, Sumon Md Shaheenur Islam, Majid Molla E, Abul Kashem Saad Bin, Nashbat Mohammad, Ashraf Azad, Khandakar Amith, Kunju Ali K Ansaruddin, Hasan-Zia Mazhar, Chowdhury Muhammad E H
Department of Biomedical Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh; Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; Computer Applications Department, Academic Bridge Program, Qatar Foundation, Doha, Qatar.
Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; Computer Applications Department, Academic Bridge Program, Qatar Foundation, Doha, Qatar.
Waste Manag. 2025 Feb 1;193:363-375. doi: 10.1016/j.wasman.2024.12.023. Epub 2024 Dec 19.
Efficient waste management is essential to minimizing environmental harm as well as encouraging sustainable progress. The escalating volume and sophistication of waste present significant challenges, prompting innovative methods for effective waste categorization and management. Deep learning models have become highly intriguing tools for automating trash categorization activities, providing effective ways to optimize processes for handling waste. Ourwork presents a novel deep learning method for trash classification, with the goal to improve the accuracy, also efficiency of garbage image categorization. We examined the effectiveness of several pre-trained models, such as InceptionV2, Densenet201, MobileNet v2, and Resnet18, using objective evaluation and cross-validation. We proposed an Eco Cycle Classifier Deep Neural Network (ECCDN-Net) model that is particularly built for the categorization of waste images. ECCDN-Net utilizes the advantageous qualities of Densenet201 and Resnet18 by merging their capacities to extract features, enhanced with auxiliary outputs to optimize the classification procedure. The set of imagesused in this study comprises 24,705 images that are divided into two distinct classes: Organic and Recyclable. The set allows extensive evaluation and training of deep learning models for waste classification of images tasks. Our research demonstrates that the ECCDN-Net model classifies waste images with 96.10% accuracy, outperforming other pre-trained models. Resnet18 had 92.68% accuracy, MobileNet v2 93.27%, Inception v3 94.77%, and Densenet201, a significant improvement, 95.98%. ECCDN-Net outperformed these models in waste image categorization with 96.10% accuracy. We ensure the reliability and generalizability of our methods throughout the dataset by integrating and cross-validating deep learning models. The current work introduces an innovative deep learning-based approach that has promising potential for waste categorization and management strategies.
高效的废物管理对于将环境危害降至最低以及促进可持续发展至关重要。废物数量的不断增加和复杂性带来了重大挑战，促使人们采用创新方法进行有效的废物分类和管理。深度学习模型已成为用于自动进行垃圾分类活动的极具吸引力的工具，为优化废物处理流程提供了有效途径。我们的工作提出了一种用于垃圾分类的新型深度学习方法，目标是提高垃圾图像分类的准确性和效率。我们使用客观评估和交叉验证，检验了几种预训练模型的有效性，如InceptionV2、Densenet201、MobileNet v2和Resnet18。我们提出了一种生态循环分类器深度神经网络（ECCDN-Net）模型，该模型专门为废物图像分类而构建。ECCDN-Net通过融合Densenet201和Resnet18提取特征的能力，并通过辅助输出进行增强以优化分类过程，从而利用了它们的优势特性。本研究中使用的图像集包含24,705张图像，分为两个不同的类别：有机和可回收。该图像集允许对用于图像任务的废物分类的深度学习模型进行广泛评估和训练。我们的研究表明，ECCDN-Net模型对废物图像的分类准确率为96.10%，优于其他预训练模型。Resnet18的准确率为92.68%，MobileNet v2为93.27%，Inception v3为94.77%，而Densenet201有显著提高，为95.98%。ECCDN-Net在废物图像分类中以96.10%的准确率优于这些模型。我们通过集成和交叉验证深度学习模型，确保了我们方法在整个数据集中的可靠性和通用性。当前的工作引入了一种基于深度学习的创新方法，在废物分类和管理策略方面具有广阔的潜力。
